We present empirical measurements of the packet delivery performance of the Telos and MicaZ sensor platforms. At a high level, their behavior is similar to that of earlier platforms. They exhibit link asymmetry, a reception “grey region, ” and temporal variations in packet loss. Looking more deeply, however, there are subtle differences, and looking deeper still, the patterns behind these complexities become clear. Packet losses are highly correlated over short time periods, but are independent over longer periods. Environmental noise (802.11b) has high spatial correlation. Packet loss occurs when a receiver operating near its noise floor experiences a small decrease in received signal strength, rather than an increase in environmental noise. These variations cause the reception “grey region. ” While short-term link asymmetries are not uncommon, long-term asymmetries are rare. Based on these findings, we suggest several ways in which current practices could be easily changed that would greatly improve the efficiency, performance, and lifetime of sensor networks. 1

Measuring 802.15.4 reception in three testbeds, we find that most intermediate links are bursty: they shift between poor and good delivery. We present a metric to measure this link burstiness and name it β. We find that link burstiness affects protocol performance and that β can predict the effects. We show that measuring β allows us to reason about how long a protocol should pause after encountering a packet failure to reduce its transmission cost. We find that using β as a guide to setting a single constant in a standard sensor network data collection protocol reduces its average transmission cost by 15%. In addition to data from 802.15.4 testbeds, we examine traces from 802.11b networks and find β has a broader relevance in the wireless domain. 1

Abstract – Localization in wireless sensor networks gets more and more important, because many applications need to locate the source of incoming measurements as precise as possible. Weighted Centroid Localization (WCL) provides a fast and easy algorithm to locate devices in wireless sensor networks. The algorithm is derived from a centroid determination which calculates the position of devices by averaging the coordinates of known reference points. To improve the calculated position in real implementations, WCL uses weights to attract the estimated position to close reference points provided that coarse distances are available. Due to the fact that Zigbee provides the Link Quality Indication (LQI) as a quality indicator of a received packet, it can also be used to estimate a distance from a node to reference points.

Most currently deployed sensor networks use the same channel to communicate information among nodes. This is a source of great inefficiency as it poorly utilizes the available wireless spectrum. This paper takes advantage of radio capabilities of MicaZ motes that can communicate on multiple frequencies as specified in the 802.15.4 standard. We consider the case of a data collection sensor network where multiple base-stations are responsible for draining data from sensor nodes. A key question becomes how to assign nodes to wireless channels such that network throughput is maximized. The problem is reduced to one of load balancing. A control theoretical approach is used to design a self-regulating load-balancing algorithm that maximizes total network throughput. It is evaluated both in simulation and on an experimental testbed. The results demonstrate a significant performance improvement. It is shown that a control theory approach is indeed needed to guarantee stability in data collection networks and prevent undue oscillation of nodes among different wireless channels upon dynamic changes in load conditions.

User needs and technology availability drive the introduction of wireless sensing applications in clinical environments. While these applications have the potential to improve efficiency and quality of care, very little is known about their performance during day-to-day use at the hospital. In this work, we use data from a deployment of a 802.15.4-based wireless sensor network at the Emergency Room of the Johns Hopkins hospital to answer these questions. Specifically, over a period of ten days we deployed a system of wireless vital signs monitors that measure the heart rate and blood oxygen levels of Emergency Room patients. During this time we collected statistics about the network’s RF links, the performance of its tree routing protocol, and its end-to-end reliability. We find that the hospital environment we tested has considerably higher radio noise levels across multiple frequency channels and more bursty links compared to other indoor environments. Nonetheless, the routing protocol we use finds high quality links and the end-to-end packet reception ratio is above 99.9%. Taken as a whole, these preliminary results suggest that despite the challenges that clinical environments pose, wireless medical sensing applications can perform well in these conditions. 1.

Abstract. Future mobile sensing systems are being designed using 802.15.4 low-power short-range radios for a diverse set of devices from embedded mobile motes to sensor-enabled cellphones in support, for example, of people-centric sensing applications. However, there is little known about the use of 802.15.4 in mobile sensor settings nor its impact on the performance of future communication architectures. We present a set of initial results from a simple yet systematic set of benchmark experiments that offer a number of important insights into the radio characteristics of mobile 802.15.4 person-to-person communication. Our results show that the body factor- that is to say, the human body and where sensors are located on the body (e.g., on the chest, foot, in the pocket)- has a significant effect on the performance of the communications system. While this phenomenon has been discussed in the context of other radios (e.g., cellular, WiFi, UWB) its impact on 802.15.4 based mobile sensor networks is not understood. Other findings that also serve to limit the communication performance include the effective contact times between mobile nodes, and, what we term the zero bandwidth crossing, which is a product of mobility and the body factor. This paper presents a set of initial findings and insights on this topic, and importantly, we consider the impact of these findings on the design of future communication architectures for mobile sensing. 1

Topology control can reduce power consumption and channel contention in wireless sensor networks by adjusting the transmission power. However, topology control for wireless sensor networks faces significant challenges, especially in indoor environments where wireless characteristics are extremely complex and dynamic. We first provide insights on the design of robust topology control schemes based on an empirical study in an office building. For example, our analysis shows that Received Signal Strength Indicator and Link Quality Indicator are not always robust indicators of Packet Reception Rate in indoor environments due to significant multi-path effects. We then present Adaptive and Robust Topology control (ART), a novel and practical topology control algorithm with several salient features: (1) ART is robust in indoor environments as it does not rely on simplifying assumptions about the wireless properties; (2) ART can adapt to variations in both link quality and contention; (3) ART introduces zero communication overhead for applications which already use acknowledgements. We have implemented ART as a topology layer in TinyOS 2.x. Our topology layer only adds 12 bytes of RAM per neighbor and 1.5 kilobytes of ROM, and requires minimal changes to upper-layer routing protocols. The advantages of ART have been demonstrated through empirical results on a 28-node indoor testbed.

RACNet is a sensor network for monitoring a data center’s environmental conditions. The high spatial and temporal fidelity measurements that RACNet provides can be used to improve the data center’s safety and energy efficiency. RACNet overcomes the network’s large scale and density and the data center’s harsh RF environment to achieve data yields of 99 % or higher over a wide range of network sizes and sampling frequencies. It does so through a novel Wireless Reliable Acquisition Protocol (WRAP). WRAP decouples topology control from data collection and implements a token passing mechanism to provide network-wide arbitration. This congestion avoidance philosophy is conceptually different from existing congestion control algorithms that retroactively respond to congestion. Furthermore, WRAP adaptively distributes nodes among multiple frequency channels to balance load and lower data latency. Results from two testbeds and an ongoing production data center deployment indicate that RACNet outperforms previous data collection systems, especially as network load increases.

Accurate estimation of link quality is the key to enable efficient routing in wireless sensor networks. Current link estimators focus mainly on identifying long-term stable links for routing. They leave out a potentially large set of intermediate links offering significant routing progress. Fine-grained analysis of link qualities reveals that such intermediate links are bursty, i.e., stable in the short term. In this paper, we use short-term estimation of wireless links to accurately identify short-term stable periods of transmission on bursty links. Our approach allows a routing protocol to forward packets over bursty links if they offer better routing progress than long-term stable links. We integrate a Short Term Link Estimator and its associated routing strategy with a standard routing protocol for sensor networks. Our evaluation reveals an average of 19 % and a maximum of 42 % reduction in the overall transmissions when routing over long-range bursty links. Our approach is not tied to any specific routing protocol and integrates seamlessly with existing routing protocols and link estimators.

Abstract—We study the distributed desynchronization problem for graphs with arbitrary topology. Motivated by the severe computational limitations of sensor networks, we present a randomized algorithm for network desynchronization that uses an extremely lightweight model of computation, while being robust to link volatility and node failure. These techniques also provide novel, ultra-lightweight randomized algorithms for quickly computing distributed vertex colorings using an asymptotically optimal number of colors. I.